It is a major challenge to the data mining community to mine the ever-growing amount of streaming data. Indeed, there are problems related to stream data classification. For example, two problems related to data streams involve “infinite length” and “concept-drift”. Since data streams have infinite length, traditional multi-pass learning processes are not applicable as they would require infinite storage and training time. Concept-drift occurs in the stream when the underlying concept of the data changes over time. Another example of a problem or failure in current data stream classification techniques involves “concept-evolution”, meaning, emergence of a novel class. Some of the existing solutions assume that the total number of classes in the data stream is fixed. But in real world data stream classification problems, such as intrusion detection, text classification, and fault detection, novel classes may appear at any time in the stream (e.g. a new intrusion). Traditional data stream classification techniques may be unable to detect the novel class until the classification models are trained with labeled instances of the novel class. Thus, novel class instances may go undetected (i.e., misclassified) until the novel class is manually detected by experts, and training data with the instances of that class is made available to the learning process. These examples of problems illustrate some of the current failures in the field of data stream, classification, though improvement in other areas is needed as well.